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Background <p>Esophageal squamous cell carcinoma (ESCC) is a major cause of cancer-related mortality worldwide, with a high prevalence and poor prognosis in specific regions. Despite advancements in clinical care, the identification of reliable biomarkers for accurate survival prediction remains a significant challenge, hindering personalized treatment.</p> Methods <p>This study utilized genomic and clinical data from TCGA and GEO databases, applying ten machine learning algorithms to develop a prognostic model based on lipid metabolism-related genes. The Random Survival Forest (RSF) algorithm was identified as the optimal framework, supplemented by immune infiltration analysis and in vitro functional validation of the key gene ACOT9.</p> Results <p>The resulting 33-gene signature achieved superior performance, with a C-index of 0.708 and survival prediction area under the curve (AUC) values approaching 1.0 in the training cohort. High-risk scores were significantly correlated with advanced histological grade, tumor stage, and increased regulatory T-cell infiltration. Furthermore, silencing of ACOT9 markedly suppressed the proliferation, migration, and invasion of ESCC cells.</p> Conclusions <p>This study provides a robust framework for ESCC risk stratification and identifies ACOT9 as a critical oncogenic driver, offering a novel scientific basis for optimizing clinical management strategies.</p>

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Establishment of prognostic prediction model based on lipid metabolism related genes in esophageal squamous cell carcinoma by machine learning algorithms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月12日

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  • Peng Wang,
  • Zhenyuan Feng,
  • Weigang Chen

摘要

Background

Esophageal squamous cell carcinoma (ESCC) is a major cause of cancer-related mortality worldwide, with a high prevalence and poor prognosis in specific regions. Despite advancements in clinical care, the identification of reliable biomarkers for accurate survival prediction remains a significant challenge, hindering personalized treatment.

Methods

This study utilized genomic and clinical data from TCGA and GEO databases, applying ten machine learning algorithms to develop a prognostic model based on lipid metabolism-related genes. The Random Survival Forest (RSF) algorithm was identified as the optimal framework, supplemented by immune infiltration analysis and in vitro functional validation of the key gene ACOT9.

Results

The resulting 33-gene signature achieved superior performance, with a C-index of 0.708 and survival prediction area under the curve (AUC) values approaching 1.0 in the training cohort. High-risk scores were significantly correlated with advanced histological grade, tumor stage, and increased regulatory T-cell infiltration. Furthermore, silencing of ACOT9 markedly suppressed the proliferation, migration, and invasion of ESCC cells.

Conclusions

This study provides a robust framework for ESCC risk stratification and identifies ACOT9 as a critical oncogenic driver, offering a novel scientific basis for optimizing clinical management strategies.